IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v12y2003i10p837-848.html
   My bibliography  Save this article

Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation

Author

Listed:
  • Jonathan Karnon

Abstract

Markov models have traditionally been used to evaluate the cost‐effectiveness of competing health care technologies that require the description of patient pathways over extended time horizons. Discrete event simulation (DES) is a more flexible, but more complicated decision modelling technique, that can also be used to model extended time horizons. Through the application of a Markov process and a DES model to an economic evaluation comparing alternative adjuvant therapies for early breast cancer, this paper compares the respective processes and outputs of these alternative modelling techniques. DES displays increased flexibility in two broad areas, though the outputs from the two modelling techniques were similar. These results indicate that the use of DES may be beneficial only when the available data demonstrates particular characteristics. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Jonathan Karnon, 2003. "Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation," Health Economics, John Wiley & Sons, Ltd., vol. 12(10), pages 837-848, October.
    • Handle: RePEc:wly:hlthec:v:12:y:2003:i:10:p:837-848
    DOI: 10.1002/hec.770
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.770
    Download Restriction: no
    File URL: https://libkey.io/10.1002/hec.770?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Joanne Lord & Maxwell A. Asante, 1999. "Estimating uncertainty ranges for costs by the bootstrap procedure combined with probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(4), pages 323-333, June.
    2. James C. Felli & Gordon B. Hazen, 1999. "A Bayesian approach to sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 263-268, May.
    3. Elizabeth Fenwick & Karl Claxton & Mark Sculpher & Andrew Briggs, 2000. "Improving the efficiency and relevance of health technology assessent: the role of iterative decision analytic modelling," Working Papers 179chedp, Centre for Health Economics, University of York.
    4. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261, May.
    5. David J. Pasta & Jennifer L. Taylor & James M. Henning, 1999. "Probabilistic Sensitivity Analysis Incorporating the Bootstrap," Medical Decision Making, , vol. 19(3), pages 353-363, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karnon, Jonathan, 2002. "Planning the efficient allocation of research funds: an adapted application of a non-parametric Bayesian value of information analysis," Health Policy, Elsevier, vol. 61(3), pages 329-347, September.
    2. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    3. A. E. Ades & A. J. Sutton, 2006. "Multiparameter evidence synthesis in epidemiology and medical decision-making: current approaches," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 5-35.
    4. James C. Felli & Gordon B. Hazen, 2004. "Javelin Diagrams: A Graphical Tool for Probabilistic Sensitivity Analysis," Decision Analysis, INFORMS, vol. 1(2), pages 93-107, June.
    5. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    6. Elisabeth Fenwick & Bernie J. O'Brien & Andrew Briggs, 2004. "Cost‐effectiveness acceptability curves – facts, fallacies and frequently asked questions," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 405-415, May.
    7. Doug Coyle & Jeremy Oakley, 2008. "Estimating the expected value of partial perfect information: a review of methods," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 9(3), pages 251-259, August.
    8. David J. Vanness & W. Ray Kim, 2002. "Bayesian estimation, simulation and uncertainty analysis: the cost‐effectiveness of ganciclovir prophylaxis in liver transplantation," Health Economics, John Wiley & Sons, Ltd., vol. 11(6), pages 551-566, September.
    9. F. J. Vázquez‐Polo & M. A. Negrín Hernández & B. González López‐Valcárcel, 2005. "Using covariates to reduce uncertainty in the economic evaluation of clinical trial data," Health Economics, John Wiley & Sons, Ltd., vol. 14(6), pages 545-557, June.
    10. Douglas Coyle, 2003. "Determining the optimal combinations of mutually exclusive interventions: a response to Hutubessy and colleagues," Health Economics, John Wiley & Sons, Ltd., vol. 12(2), pages 159-162, February.
    11. Anna Heath & Ioanna Manolopoulou & Gianluca Baio, 2017. "A Review of Methods for Analysis of the Expected Value of Information," Medical Decision Making, , vol. 37(7), pages 747-758, October.
    12. Nicky Welton & A. E. Ades, 2012. "Research Decisions In The Face Of Heterogeneity: What Can A New Study Tell Us?," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1196-1200, October.
    13. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
    14. Pedram Sendi & Huldrych F Günthard & Mathew Simcock & Bruno Ledergerber & Jörg Schüpbach & Manuel Battegay & for the Swiss HIV Cohort Study, 2007. "Cost-Effectiveness of Genotypic Antiretroviral Resistance Testing in HIV-Infected Patients with Treatment Failure," PLOS ONE, Public Library of Science, vol. 2(1), pages 1-8, January.
    15. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of ‘payback’ and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357, December.
    16. Phillip Dinh & Xiao-Hua Zhou, 2006. "Nonparametric Statistical Methods for Cost-Effectiveness Analyses," Biometrics, The International Biometric Society, vol. 62(2), pages 576-588, June.
    17. Mark J. Sculpher & Karl Claxton & Mike Drummond & Chris McCabe, 2006. "Whither trial‐based economic evaluation for health care decision making?," Health Economics, John Wiley & Sons, Ltd., vol. 15(7), pages 677-687, July.
    18. Michał Jakubczyk & Bogumił Kamiński, 2010. "Cost‐effectiveness acceptability curves – caveats quantified," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 955-963, August.
    19. Manuel Gomes & Richard Grieve & Richard Nixon & W. J. Edmunds, 2012. "Statistical Methods for Cost-Effectiveness Analyses That Use Data from Cluster Randomized Trials," Medical Decision Making, , vol. 32(1), pages 209-220, January.
    20. Anna Heath & Ioanna Manolopoulou & Gianluca Baio, 2018. "Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching," Medical Decision Making, , vol. 38(2), pages 163-173, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:hlthec:v:12:y:2003:i:10:p:837-848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.